Process parameter optimization and characterization of cold spray pure and blended AA6061 powder depositions
Bibliographic record
Abstract
Cold spray is a solid-state deposition method belonging in the thermal spray group of technologies that creates coatings, mass restorations, and additively manufactured components by accelerating feedstock powders at supersonic speeds via a de Laval nozzle. Once accelerated particles collide with a substrate or existing layer build up, severe plastic deformation from impact creates mechanical and metallurgical bonding. Among the many materials compatible with cold spray, aluminum 6061 alloy is a widely used, a general-purpose metal commonly found in industries such as automotive and aerospace as a structural material. Typically, metallic powders are manufactured with gas atomization and available as pure AA6061, or as a blend with various ceramics to obtain desired deposition mechanical, material, and manufacturing requirements. Additionally, a solid-state powder manufacturing method using mechanical grinding has emerged providing cold spray users with AA6061 powders of different morphology and metallurgy more like AA6061 bulk material. This study investigates deposition properties for pure gas atomized and ground AA6061 powders, and gas atomized powders blended with Al2O3, SiO2, and ZrO2. Cold spray depositions are characterized by studying their deposition efficiency, thickness, density, and microhardness. Effects of powder size distribution, morphology, and blending are correlated with deposition characteristics. Observations made include higher deposition efficiency and thickness with blended powders, and general hardness and deposition efficiency tradeoff for gas atomized powders, and high deposition efficiency and hardness for ground powder. Response Surface Methodology is used to determine optimum temperature and pressure conditions for powders, with deposition efficiency, thickness, and microhardness explanatory variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".